Competent Triple Identification for Knowledge Graph Completion under the Open-World Assumption

• FARJANA Esrat

  The Graduate University for Advanced Studies, SOKENDAI

• KERTKEIDKACHORN Natthawut

  Japan Advanced Institute of Science and Technology

• ICHISE Ryutaro

  The Graduate University for Advanced Studies, SOKENDAI National Institute of Informatics National Institute of Advanced Industrial Science and Technology

Abstract

The usefulness and usability of existing knowledge graphs (KGs) are mostly limited because of the incompleteness of knowledge compared to the growing number of facts about the real world. Most existing ontology-based KG completion methods are based on the closed-world assumption, where KGs are fixed. In these methods, entities and relations are defined, and new entity information cannot be easily added. In contrast, in open-world assumptions, entities and relations are not previously defined. Thus there is a vast scope to find new entity information. Despite this, knowledge acquisition under the open-world assumption is challenging because most available knowledge is in a noisy unstructured text format. Nevertheless, Open Information Extraction (OpenIE) systems can extract triples, namely (head text; relation text; tail text), from raw text without any prespecified vocabulary. Such triples contain noisy information that is not essential for KGs. Therefore, to use such triples for the KG completion task, it is necessary to identify competent triples for KGs from the extracted triple set. Here, competent triples are the triples that can contribute to add new information to the existing KGs. In this paper, we propose the Competent Triple Identification (CTID) model for KGs. We also propose two types of feature, namely syntax- and semantic-based features, to identify competent triples from a triple set extracted by a state-of-the-art OpenIE system. We investigate both types of feature and test their effectiveness. It is found that the performance of the proposed features is about 20% better compared to that of the RᴇVᴇʀʙ system in identifying competent triples.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E105.D (3), 646-655, 2022-03-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• knowledge extraction
• information retrieval
• competent triple
• knowledge graph completion

Details

• CRID: 1390573242440775296
• NII Article ID: 130008165621
• DOI: 10.1587/transinf.2021edp7148
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E105.D/3/E105.D_2021EDP7148/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed